{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "                                                   0\n",
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       "4  162943\\t/front-api/bill/create\\t3\\t568.89\\t138..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Download/log.txt',header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
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       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
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       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
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       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
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       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Download/log.txt',header = None,sep = '\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>101033</th>\n",
       "      <td>7495653</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>314.78</td>\n",
       "      <td>98.51</td>\n",
       "      <td>216.27</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-02 00:22:42</td>\n",
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       "      <th>26223</th>\n",
       "      <td>2506872</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-12-01 16:13:09</td>\n",
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       "    <tr>\n",
       "      <th>174874</th>\n",
       "      <td>13077412</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1748.54</td>\n",
       "      <td>118.72</td>\n",
       "      <td>449.06</td>\n",
       "      <td>194.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-25 20:31:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117171</th>\n",
       "      <td>8666953</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>23</td>\n",
       "      <td>4771.24</td>\n",
       "      <td>104.09</td>\n",
       "      <td>739.91</td>\n",
       "      <td>207.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-20 22:12:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27257</th>\n",
       "      <td>2600514</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1485.83</td>\n",
       "      <td>76.70</td>\n",
       "      <td>279.95</td>\n",
       "      <td>165.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-02 18:53:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "101033   7495653  /front-api/bill/create      2        314.78         98.51   \n",
       "26223    2506872  /front-api/bill/create      9       1271.47         86.47   \n",
       "174874  13077412  /front-api/bill/create      9       1748.54        118.72   \n",
       "117171   8666953  /front-api/bill/create     23       4771.24        104.09   \n",
       "27257    2600514  /front-api/bill/create      9       1485.83         76.70   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "101033        216.27         157.0        60  2019-03-02 00:22:42  \n",
       "26223         187.54         141.0        60  2018-12-01 16:13:09  \n",
       "174874        449.06         194.0        60  2019-05-25 20:31:16  \n",
       "117171        739.91         207.0        60  2019-03-20 22:12:05  \n",
       "27257         279.95         165.0        60  2018-12-02 18:53:11  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('api',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-11-01 00:00:07</td>\n",
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       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
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       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head(2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-01-30 22:33:49\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval           created_at  \n",
       "created_at                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07        232.02         189.0        60  2018-11-01 00:04:07  "
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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    "df.head(5)"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <td>89.40</td>\n",
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       "      <th>2019-01-01 00:04:56</th>\n",
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       "      <td>143.42</td>\n",
       "      <td>187.13</td>\n",
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       "      <td>2019-01-01 23:57:57</td>\n",
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       "      <th>2019-01-01 23:58:57</th>\n",
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       "      <th>2019-01-01 23:59:57</th>\n",
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       "      <td>110.06</td>\n",
       "      <td>175.36</td>\n",
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       "      <td>2019-01-01 23:59:57</td>\n",
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       "                          id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                      \n",
       "2019-01-01 00:00:56  4334654      5        838.89        106.85        289.56   \n",
       "2019-01-01 00:01:56  4334717      7        932.25         79.51        168.52   \n",
       "2019-01-01 00:02:56  4334800      2        180.24         81.97         98.27   \n",
       "2019-01-01 00:03:56  4334813      5        856.20         89.40        209.12   \n",
       "2019-01-01 00:04:56  4334919      3        593.41         82.03        312.03   \n",
       "...                      ...    ...           ...           ...           ...   \n",
       "2019-01-01 23:55:57  4397160      4        584.47         89.48        242.96   \n",
       "2019-01-01 23:56:57  4397232      1         90.61         90.61         90.61   \n",
       "2019-01-01 23:57:57  4397304      3        502.90        143.42        187.13   \n",
       "2019-01-01 23:58:57  4397368      2        459.39        228.96        230.43   \n",
       "2019-01-01 23:59:57  4397449      5        710.47        110.06        175.36   \n",
       "\n",
       "                     res_time_avg  interval           created_at  \n",
       "created_at                                                        \n",
       "2019-01-01 00:00:56         167.0        60  2019-01-01 00:00:56  \n",
       "2019-01-01 00:01:56         133.0        60  2019-01-01 00:01:56  \n",
       "2019-01-01 00:02:56          90.0        60  2019-01-01 00:02:56  \n",
       "2019-01-01 00:03:56         171.0        60  2019-01-01 00:03:56  \n",
       "2019-01-01 00:04:56         197.0        60  2019-01-01 00:04:56  \n",
       "...                           ...       ...                  ...  \n",
       "2019-01-01 23:55:57         146.0        60  2019-01-01 23:55:57  \n",
       "2019-01-01 23:56:57          90.0        60  2019-01-01 23:56:57  \n",
       "2019-01-01 23:57:57         167.0        60  2019-01-01 23:57:57  \n",
       "2019-01-01 23:58:57         229.0        60  2019-01-01 23:58:57  \n",
       "2019-01-01 23:59:57         142.0        60  2019-01-01 23:59:57  \n",
       "\n",
       "[880 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-01-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.drop(['id','interval'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   count         179496 non-null  int64  \n",
      " 1   res_time_sum  179496 non-null  float64\n",
      " 2   res_time_min  179496 non-null  float64\n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_avg  179496 non-null  float64\n",
      " 5   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22ce7a5efd0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7wPIk5yf5BWAtsKPPc5Kkd5WBOGRUVRNJPgn8C7AA+FJV7T2OVR31cNIQspfBM1/6AHsZVH3tZSBOKkuS+m9QDhlJkvrMQJAkAfMoEObTR18k2Z9kT5Ink+zu93x6leRLSQ4l+WFX7YwkDyd5tl2f3s859uoovXw2yY/adnkyyUf7OcdeJDkvybeSPJ1kb5IbW33otssMvQzjdnlvkseTfL/18rlW7+t2mRfnENpHX/wnXR99AXxsWD/6Isl+YGVVDdWbbZL8LjAO3F1VF7fa3wKvVNWWFtSnV9Vf93OevThKL58Fxqvq7/o5t2ORZAmwpKq+l+T9wBPAVcB1DNl2maGXP2P4tkuAxVU1nuQU4DvAjcCf0sftMl/2EP7voy+q6n+AyY++0ElUVd8GXplSXgNsa7e30fkFHnhH6WXoVNXBqvpeu/068DRwLkO4XWboZehUx3i7e0q7FH3eLvMlEM4FXui6f4Ah/UFpCvjXJE+0j+sYZiNVdRA6v9DAOX2ez4n6ZJIftENKA3+YpVuSZcCHgF0M+XaZ0gsM4XZJsiDJk8Ah4OGq6vt2mS+B0NNHXwyRD1fVbwF/CNzQDl+o/+4AfhW4BDgI3NLf6fQuyfuArwGfqqqf9ns+J2KaXoZyu1TV21V1CZ1PZrgsycX9ntN8CYR59dEXVfViuz4EfIPOIbFh9VI79jt5DPhQn+dz3KrqpfZL/HPgHxiS7dKOUX8N+HJVfb2Vh3K7TNfLsG6XSVX1E2AMWE2ft8t8CYR589EXSRa3E2YkWQz8AfDDmUcNtB3AunZ7HfBAH+dyQiZ/UZs/YQi2Szt5eSfwdFV9vuuhodsuR+tlSLfL2Uk+2G4vAj4C/Ad93i7z4lVGAO2lZl/g/z/6YnOfp3RckvwKnb0C6Hy0yFeGpZckXwVG6XyE70vAZ4B/BrYDvwQ8D1xTVQN/svYovYzSOSxRwH7gLyeP9w6qJL8D/BuwB/h5K3+azrH3odouM/TyMYZvu/wGnZPGC+j8Y769qv4myZn0cbvMm0CQJJ2Y+XLISJJ0ggwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSp+V9t69IhYiTk4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22ce956d3a0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22ceac7d1c0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-3-1']['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df['2019-3-1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df2[%6677### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <td>2.957447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 01:00:00</th>\n",
       "      <td>1.347826</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 02:00:00</th>\n",
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       "      <th>2019-03-01 05:00:00</th>\n",
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       "      <th>2019-03-01 06:00:00</th>\n",
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       "      <th>2019-03-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 09:00:00</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 10:00:00</th>\n",
       "      <td>1.500000</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 11:00:00</th>\n",
       "      <td>1.516129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 12:00:00</th>\n",
       "      <td>4.051724</td>\n",
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       "      <th>2019-03-01 13:00:00</th>\n",
       "      <td>6.983333</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 14:00:00</th>\n",
       "      <td>7.233333</td>\n",
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       "      <th>2019-03-01 15:00:00</th>\n",
       "      <td>8.933333</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 16:00:00</th>\n",
       "      <td>7.666667</td>\n",
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       "      <th>2019-03-01 17:00:00</th>\n",
       "      <td>5.864407</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 18:00:00</th>\n",
       "      <td>6.633333</td>\n",
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       "      <th>2019-03-01 19:00:00</th>\n",
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       "      <th>2019-03-01 20:00:00</th>\n",
       "      <td>10.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 21:00:00</th>\n",
       "      <td>11.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 22:00:00</th>\n",
       "      <td>8.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 23:00:00</th>\n",
       "      <td>5.333333</td>\n",
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      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-03-01 00:00:00   2.957447\n",
       "2019-03-01 01:00:00   1.347826\n",
       "2019-03-01 02:00:00   1.000000\n",
       "2019-03-01 03:00:00        NaN\n",
       "2019-03-01 04:00:00        NaN\n",
       "2019-03-01 05:00:00   1.000000\n",
       "2019-03-01 06:00:00        NaN\n",
       "2019-03-01 07:00:00        NaN\n",
       "2019-03-01 08:00:00        NaN\n",
       "2019-03-01 09:00:00        NaN\n",
       "2019-03-01 10:00:00   1.500000\n",
       "2019-03-01 11:00:00   1.516129\n",
       "2019-03-01 12:00:00   4.051724\n",
       "2019-03-01 13:00:00   6.983333\n",
       "2019-03-01 14:00:00   7.233333\n",
       "2019-03-01 15:00:00   8.933333\n",
       "2019-03-01 16:00:00   7.666667\n",
       "2019-03-01 17:00:00   5.864407\n",
       "2019-03-01 18:00:00   6.633333\n",
       "2019-03-01 19:00:00   8.866667\n",
       "2019-03-01 20:00:00  10.183333\n",
       "2019-03-01 21:00:00  11.300000\n",
       "2019-03-01 22:00:00   8.400000\n",
       "2019-03-01 23:00:00   5.333333"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['count']].resample('1H').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
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       "    <tr>\n",
       "      <th>created_at</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:00:41</th>\n",
       "      <td>9</td>\n",
       "      <td>2750.62</td>\n",
       "      <td>108.90</td>\n",
       "      <td>1243.21</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2019-03-01 00:00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:01:41</th>\n",
       "      <td>10</td>\n",
       "      <td>2680.90</td>\n",
       "      <td>109.79</td>\n",
       "      <td>621.66</td>\n",
       "      <td>268.0</td>\n",
       "      <td>2019-03-01 00:01:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:02:41</th>\n",
       "      <td>3</td>\n",
       "      <td>375.49</td>\n",
       "      <td>69.80</td>\n",
       "      <td>158.44</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2019-03-01 00:02:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:04:41</th>\n",
       "      <td>1</td>\n",
       "      <td>133.46</td>\n",
       "      <td>133.46</td>\n",
       "      <td>133.46</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2019-03-01 00:04:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:05:41</th>\n",
       "      <td>4</td>\n",
       "      <td>431.09</td>\n",
       "      <td>76.27</td>\n",
       "      <td>130.19</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2019-03-01 00:05:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 23:54:42</th>\n",
       "      <td>2</td>\n",
       "      <td>309.93</td>\n",
       "      <td>103.34</td>\n",
       "      <td>206.59</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2019-03-01 23:54:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 23:55:42</th>\n",
       "      <td>3</td>\n",
       "      <td>405.70</td>\n",
       "      <td>110.04</td>\n",
       "      <td>180.07</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2019-03-01 23:55:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 23:56:42</th>\n",
       "      <td>6</td>\n",
       "      <td>836.89</td>\n",
       "      <td>96.77</td>\n",
       "      <td>287.68</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2019-03-01 23:56:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 23:57:42</th>\n",
       "      <td>3</td>\n",
       "      <td>1365.71</td>\n",
       "      <td>113.55</td>\n",
       "      <td>984.55</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2019-03-01 23:57:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 23:59:42</th>\n",
       "      <td>3</td>\n",
       "      <td>339.55</td>\n",
       "      <td>110.39</td>\n",
       "      <td>115.77</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2019-03-01 23:59:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>821 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-03-01 00:00:41      9       2750.62        108.90       1243.21   \n",
       "2019-03-01 00:01:41     10       2680.90        109.79        621.66   \n",
       "2019-03-01 00:02:41      3        375.49         69.80        158.44   \n",
       "2019-03-01 00:04:41      1        133.46        133.46        133.46   \n",
       "2019-03-01 00:05:41      4        431.09         76.27        130.19   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-03-01 23:54:42      2        309.93        103.34        206.59   \n",
       "2019-03-01 23:55:42      3        405.70        110.04        180.07   \n",
       "2019-03-01 23:56:42      6        836.89         96.77        287.68   \n",
       "2019-03-01 23:57:42      3       1365.71        113.55        984.55   \n",
       "2019-03-01 23:59:42      3        339.55        110.39        115.77   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-03-01 00:00:41         305.0  2019-03-01 00:00:41  \n",
       "2019-03-01 00:01:41         268.0  2019-03-01 00:01:41  \n",
       "2019-03-01 00:02:41         125.0  2019-03-01 00:02:41  \n",
       "2019-03-01 00:04:41         133.0  2019-03-01 00:04:41  \n",
       "2019-03-01 00:05:41         107.0  2019-03-01 00:05:41  \n",
       "...                           ...                  ...  \n",
       "2019-03-01 23:54:42         154.0  2019-03-01 23:54:42  \n",
       "2019-03-01 23:55:42         135.0  2019-03-01 23:55:42  \n",
       "2019-03-01 23:56:42         139.0  2019-03-01 23:56:42  \n",
       "2019-03-01 23:57:42         455.0  2019-03-01 23:57:42  \n",
       "2019-03-01 23:59:42         113.0  2019-03-01 23:59:42  \n",
       "\n",
       "[821 rows x 6 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,3))\n",
    "df2['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cebc89c10>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-3-1'][['count']].boxplot(showmeans = True,meanline = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cebc24580>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-3-1']['res_time_avg'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cebc19d00>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9gyuAt9L2o8Cymv3bgTdSvX2K+gkiYggYguozlf0tzOYj9xCsWc16UjkitkZEe0SsoDpZ/O2I+DSwE+hLm/UBT6TlncBGSYskXUl18vj5NLz0rqTr0tlFt9bsY9Y0fKWyNbszcaXyl4AdkvLAQeBmgIjYJ2kHsB8YB+6IiIm0z+3Aw8D5VCeTPaFsTcVXKttCoIgph+vnvc7OztizZ0+jm2EGQC6X4ytf+Qo9PT3HhoxKpRL9/f2MjIw0unlmx0h6ISI6p1rnK5XNMuArlW0hcCCYZcBXKttC4EAwy4CvVLaFwLe/NsuAr1S2hcCTymYZ83UINp95UtnMzE7LgWBmZoADwczMEgeCmZkBDgQzM0scCGZmBjgQzMwscSCYmRngQDAzs8SBYGZmgAPBzMwSB4KZmQEOBDMzSxwIZmYGOBDMMlMsFsnlcqxZs4ZcLkexWGx0k8xmxA/IMctAsVhkYGCAQqHAxMQELS0t5PN5AD8kx5qGewhmGRgcHKRQKNDT00Nrays9PT0UCgUGBwcb3TSzaXMgmGWgUqnw6KOPct5559HT08N5553Ho48+SqVSaXTTzKbNQ0ZmGbj44osZGhri7rvvZtWqVezfv58vfOELXHzxxY1umtm0ORDMMvDOO++wZMkSVq9ezcTEBKtXr2bJkiW88847jW6a2bTNeshI0jJJJUkVSfskbU71SyXtkvRy+nlJzT5bJR2Q9JKktTX1ayTtTevuk6T6fi2zuTU+Ps4999xDf38/a9eupb+/n3vuuYfx8fFGN81s2uqZQxgH/m1EdADXAXdIWgXcCeyOiJXA7vSetG4jcBWwDnhAUks61oPAJmBleq2ro11mc27RokW8/fbbjIyMsHv3bkZGRnj77bdZtGhRo5tmNm2zHjKKiEPAobT8rqQKsBRYD3SnzbYBZWBLqj8SEe8Br0g6AFwr6VVgSUQ8AyBpO7ABeHK2bTOba5/97GfZsmULAKtWreLLX/4yW7Zs4XOf+1yDW2Y2fZnMIUhaAawGngPaUlgQEYckXZ42Wwo8W7PbaKodScuT61N9ziaqPQna2tool8tZNN+sbjfddBOjo6PceeedHDlyhHPOOYcbb7yRm266yX+n1jTqDgRJFwJ/Cnw+It45xfD/VCviFPUTixFDwBBAZ2dndHd3z7i9ZmfK0b/HcrmM/zatGdV1HYKkc6iGwdci4s9S+U1JV6T1VwBvpfoosKxm93bgjVRvn6Ju1lR86wprdvWcZSSgAFQi4ss1q3YCfWm5D3iipr5R0iJJV1KdPH4+DS+9K+m6dMxba/YxawrFYpHNmzdz+PBhIoLDhw+zefNmh4I1FUVMOTpz+h2lLuAvgb3AB6n876jOI+wAlgMHgZsj4qdpnwHgNqpnKH0+Ip5M9U7gYeB8qpPJ/XGahnV2dsaePXtm1XazrC1btozx8XG+/vWvH7uX0S233EJrayuvv/56o5tndoykFyKic8p1sw2ERnMg2HwiiaeeeoqPf/zjx+YQdu3axQ033ECz/j9mC9OpAsH3MjIzM8CBYJaJ9vZ2+vr6KJVKjI+PUyqV6Ovro729/fQ7m80TvpeRWQbuvvtuNm/ezG233cbBgwdZvnz5sdtZmDUL9xDMMtDb28u9997L4sWLAVi8eDH33nuvH45jTcWTymYZ84VpNp95UtlsDvjCNGt2DgSzDPjCNFsIPGRklgFfmGbNwkNGZmfY6Ogo27dvp6enh9bWVnp6eti+fTujo6On39lsnnAgmJkZ4EAwy4QvTLOFwBemmWXAF6bZQuAeglkGfGGaLQQ+y8gsY74wzeYzn2VkZman5UAwMzPAk8pmmVm+fPlxF6EtW7aMgwcPNrBFZjPjHoJZBiaHAcDrr7/O8uXLG9Qis5lzIJhl4GS3p/BtK6yZOBDMzAxwIJhlbuvWrY1ugtmsOBDMMnT99ddz9dVXc/311ze6KWYz5rOMzE5D0rS3ffrpp3n66adntX+zXiRqC4cDwew0pvMP9an+0fc/9NYs5k0gSFoH3Au0AH8cEV9qcJNsAfrIto+ckePmHs7N+WcC7O3be8aObWefeREIklqAPwI+DowC35W0MyL2N7ZlttC8Wzlz3zNeu+vGE2q/vOUvztjnXXT+OWfs2HZ2mheBAFwLHIiIHwFIegRYDzgQLFOvfunXZ7zPTOYQJpsqJE7GQ0vWaPMlEJYCtVfwjAL/ePJGkjYBmwDa2tool8tz0jg7u5VKpRltPzY2xoUXXjjjz/HfszXafAmEqb6CnfB1KSKGgCGo3v7atxi2+ci3v7ZmNV+uQxgFltW8bwfeaFBbzMzOSvMlEL4LrJR0paRzgY3Azga3yczsrDIvhowiYlzSvwa+RfW004ciYl+Dm2VmdlaZF4EAEBHfBL7Z6HaYmZ2t5suQkZmZNZgDwczMAAeCmZklatarIyX9GHit0e0wm8JlwE8a3Qizk/jliPjQVCuaNhDM5itJeyKis9HtMJspDxmZmRngQDAzs8SBYJa9oUY3wGw2PIdgZmaAewhmZpY4EMzMDHAgmJlZ4kAwOw1Jn5d0Qc37b0q6uJFtMjsTPKlsC5qqD0RWRHxQxzFeBTojwlcf24LmHoItOJJWSKpIegD4HvDvJX1X0ouS/mPaZrGk/yHprySNSPrNkxzrt4FfAkqSSqn2qqTL0uf8UNIfp2N8TdLHJP0vSS9Lurbmsx5Kbfi+pPWnaftfSvpeel2f6t+Q9Ima7R6WdJOkCyTtSL/bNyQ9J8lXSdusuIdgC46kFcCPgOuBJcCngN+i+uzuncDdwIeAdRHx2bTPRRHxs5Mc71VqeghH3wMXAgeA1cA+qk/++ysgD3wS+ExEbJD0X4D9EfHVNNT0PLA6Ig5P8VkXAB9ExM8lrQSKEdEp6TeADRHRl54q+H+AXwHuAFZGxG9JygE/AK6LiD2z/g9oZy33EGyhei0ingVuSK/vU+0t/CqwEtgLfEzSXZJ+7WRhMA2vRMTeNCS1D9gd1W9Ze4EVaZsbgDsl/QAoA+cBy09yvHOA/yZpL/AosCrVnwT+uaRFwL8AvhMRfwt0AY8ARMQI8OIsfw+z+fPENLOMHf32LeB3I+K/Tt5A0jXAJ4DflfRURPynWXzOezXLH9S8/4C/+/9LwE0R8dI0jvc7wJvAR6l+Yfs5QOoxlIG1wG8CxZpjm2XCPQRb6L4F3CbpQgBJSyVdLumXgL+JiK8Cvw9cfYpjvAv8Qp1t6E8T3EhafYptLwIOpR7Hv6L6jPGjHgE+A/xaOibAMPAv03FXAR+po512lnMPwRa0iHhKUgfwTPr3eAz4NPBh4PckfQAcAW4/xWGGgCclHYqInlk04z8Dfwi8mELhVeDGk2z7APCnkm4GSvxdTwfgKWA7sDMi3q/ZfpukF6kOi70IzHb4y85ynlQ2a2KSWoBz0pDSPwB2A79SExhm0+Yegllzu4DqKbHnUJ1PuN1hYLPlHoJZIunPgSsnlbdExLem2r7Oz1oL3DWp/EpE/EbWn2U2XQ4EMzMDfJaRmZklDgQzMwMcCGZmljgQzMwMgP8PQ7NEABIR4wsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-3-1'][['res_time_avg']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-66-f88d75ae8636>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df2[df['res_time_avg']>1000]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01 16:31:42</th>\n",
       "      <td>4</td>\n",
       "      <td>4370.28</td>\n",
       "      <td>106.42</td>\n",
       "      <td>3132.75</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>2019-03-01 16:31:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 16:32:42</th>\n",
       "      <td>4</td>\n",
       "      <td>6457.48</td>\n",
       "      <td>353.06</td>\n",
       "      <td>3502.39</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>2019-03-01 16:32:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 17:11:42</th>\n",
       "      <td>6</td>\n",
       "      <td>6801.17</td>\n",
       "      <td>626.30</td>\n",
       "      <td>1787.49</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>2019-03-01 17:11:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 17:12:42</th>\n",
       "      <td>6</td>\n",
       "      <td>14090.51</td>\n",
       "      <td>693.01</td>\n",
       "      <td>3885.85</td>\n",
       "      <td>2348.0</td>\n",
       "      <td>2019-03-01 17:12:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 17:13:42</th>\n",
       "      <td>5</td>\n",
       "      <td>10960.85</td>\n",
       "      <td>447.54</td>\n",
       "      <td>5574.74</td>\n",
       "      <td>2192.0</td>\n",
       "      <td>2019-03-01 17:13:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 17:14:42</th>\n",
       "      <td>4</td>\n",
       "      <td>6700.27</td>\n",
       "      <td>282.57</td>\n",
       "      <td>2599.20</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>2019-03-01 17:14:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:28:42</th>\n",
       "      <td>2</td>\n",
       "      <td>7362.08</td>\n",
       "      <td>2019.15</td>\n",
       "      <td>5343.93</td>\n",
       "      <td>3681.0</td>\n",
       "      <td>2019-03-01 20:28:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:29:42</th>\n",
       "      <td>8</td>\n",
       "      <td>42945.44</td>\n",
       "      <td>2033.50</td>\n",
       "      <td>9619.60</td>\n",
       "      <td>5368.0</td>\n",
       "      <td>2019-03-01 20:29:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:30:42</th>\n",
       "      <td>2</td>\n",
       "      <td>21760.67</td>\n",
       "      <td>5659.67</td>\n",
       "      <td>16101.00</td>\n",
       "      <td>10880.0</td>\n",
       "      <td>2019-03-01 20:30:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:31:42</th>\n",
       "      <td>4</td>\n",
       "      <td>35443.95</td>\n",
       "      <td>5968.98</td>\n",
       "      <td>13971.11</td>\n",
       "      <td>8860.0</td>\n",
       "      <td>2019-03-01 20:31:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:32:42</th>\n",
       "      <td>5</td>\n",
       "      <td>42156.84</td>\n",
       "      <td>4288.39</td>\n",
       "      <td>13783.64</td>\n",
       "      <td>8431.0</td>\n",
       "      <td>2019-03-01 20:32:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:33:42</th>\n",
       "      <td>7</td>\n",
       "      <td>83667.58</td>\n",
       "      <td>2376.46</td>\n",
       "      <td>28236.13</td>\n",
       "      <td>11952.0</td>\n",
       "      <td>2019-03-01 20:33:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:34:42</th>\n",
       "      <td>5</td>\n",
       "      <td>27553.56</td>\n",
       "      <td>1836.16</td>\n",
       "      <td>8204.02</td>\n",
       "      <td>5510.0</td>\n",
       "      <td>2019-03-01 20:34:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:35:42</th>\n",
       "      <td>12</td>\n",
       "      <td>51399.50</td>\n",
       "      <td>821.26</td>\n",
       "      <td>11529.73</td>\n",
       "      <td>4283.0</td>\n",
       "      <td>2019-03-01 20:35:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 20:36:42</th>\n",
       "      <td>17</td>\n",
       "      <td>37474.45</td>\n",
       "      <td>373.98</td>\n",
       "      <td>6887.98</td>\n",
       "      <td>2204.0</td>\n",
       "      <td>2019-03-01 20:36:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-03-01 16:31:42      4       4370.28        106.42       3132.75   \n",
       "2019-03-01 16:32:42      4       6457.48        353.06       3502.39   \n",
       "2019-03-01 17:11:42      6       6801.17        626.30       1787.49   \n",
       "2019-03-01 17:12:42      6      14090.51        693.01       3885.85   \n",
       "2019-03-01 17:13:42      5      10960.85        447.54       5574.74   \n",
       "2019-03-01 17:14:42      4       6700.27        282.57       2599.20   \n",
       "2019-03-01 20:28:42      2       7362.08       2019.15       5343.93   \n",
       "2019-03-01 20:29:42      8      42945.44       2033.50       9619.60   \n",
       "2019-03-01 20:30:42      2      21760.67       5659.67      16101.00   \n",
       "2019-03-01 20:31:42      4      35443.95       5968.98      13971.11   \n",
       "2019-03-01 20:32:42      5      42156.84       4288.39      13783.64   \n",
       "2019-03-01 20:33:42      7      83667.58       2376.46      28236.13   \n",
       "2019-03-01 20:34:42      5      27553.56       1836.16       8204.02   \n",
       "2019-03-01 20:35:42     12      51399.50        821.26      11529.73   \n",
       "2019-03-01 20:36:42     17      37474.45        373.98       6887.98   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-03-01 16:31:42        1092.0  2019-03-01 16:31:42  \n",
       "2019-03-01 16:32:42        1614.0  2019-03-01 16:32:42  \n",
       "2019-03-01 17:11:42        1133.0  2019-03-01 17:11:42  \n",
       "2019-03-01 17:12:42        2348.0  2019-03-01 17:12:42  \n",
       "2019-03-01 17:13:42        2192.0  2019-03-01 17:13:42  \n",
       "2019-03-01 17:14:42        1675.0  2019-03-01 17:14:42  \n",
       "2019-03-01 20:28:42        3681.0  2019-03-01 20:28:42  \n",
       "2019-03-01 20:29:42        5368.0  2019-03-01 20:29:42  \n",
       "2019-03-01 20:30:42       10880.0  2019-03-01 20:30:42  \n",
       "2019-03-01 20:31:42        8860.0  2019-03-01 20:31:42  \n",
       "2019-03-01 20:32:42        8431.0  2019-03-01 20:32:42  \n",
       "2019-03-01 20:33:42       11952.0  2019-03-01 20:33:42  \n",
       "2019-03-01 20:34:42        5510.0  2019-03-01 20:34:42  \n",
       "2019-03-01 20:35:42        4283.0  2019-03-01 20:35:42  \n",
       "2019-03-01 20:36:42        2204.0  2019-03-01 20:36:42  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-3-1']\n",
    "df2[df['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qindi = df['2019-3-1'].resample('20T').mean()\n",
    "qindi[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-3-1':'2019-3-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday'] = df.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07    False  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday.isin((5,6))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekday')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 周末调用平均次数多，7.57\n",
    "#那个时段调用次数多\n",
    "df.groupby(['weekday',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekday',df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekday</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekday         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekday',df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekday',df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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